Iterative Interference Cancellation Using Mixed Feedback Weights and Stabilizing Step Sizes

ABSTRACT

A receiver is configured for canceling intra-cell and inter-cell interference in coded, multiple-access, spread-spectrum transmissions that propagate through frequency-selective communication channels. The receiver employs iterative symbol-estimate weighting, subtractive cancellation with a stabilizing step-size, and mixed-decision symbol estimates. Receiver embodiments may be implemented explicitly in software or programmed hardware, or implicitly in standard Rake-based hardware either within the Rake (i.e., at the finger level) or outside the Rake (i.e., at the user or sub channel symbol level).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.11/451,932, filed Jun. 13, 2006, which claims priority to U.S. PatentApplication No. 60/736,204, filed Nov. 15, 2005, both entitled“Iterative Interference Cancellation Using Mixed Feedback Weights andStabilizing Step Sizes,” and each of which is incorporated by referencein its entirety.

BACKGROUND

1. Field of the Invention

The present invention relates generally to iterative interferencecancellation in received wireless communication signals and, moreparticularly, to cancellation of intra-cell interference and/orinter-cell interference in coded spread spectrum communication systems.

2. Discussion of the Related Art

In an exemplary wireless multiple-access system, a communicationresource is divided into code-space subchannels that are allocated todifferent users. A plurality of sub channel signals received by awireless terminal (e.g., a subscriber unit or a base station) maycorrespond to different users and/or different subchannels allocated toa particular user.

If a single transmitter broadcasts different messages to differentreceivers, such as a base station in a wireless communication systembroadcasting to a plurality of mobile terminals, the channel resource issubdivided in order to distinguish between messages intended for eachmobile. Thus, each mobile terminal, by knowing its allocatedsubchannel(s), may decode messages intended for it from thesuperposition of received signals. Similarly, a base station typicallyseparates received signals into subchannels in order to differentiatebetween users.

In a multipath environment, received signals are superpositions oftime-delayed and complex-scaled versions of the transmitted signals.Multipath can cause several types of interference. Intra-channelinterference occurs when the multipath time-delays cause subchannels toleak into other sub channels. For example, in a forward link,subchannels that are orthogonal at the transmitter may not be orthogonalat the receiver. When multiple base stations (or sectors or cells) areactive, there may also be inter-channel interference caused by unwantedsignals received from other base causing a receiver to incorrectlydecode received transmissions, thus increasing a receiver's error floor.Interference may also have other deleterious effects on communications.For example, interference may lower capacity in a communication system,decrease the region of coverage, and/or decrease maximum data rates. Forthese reasons, a reduction in interference can improve reception ofselected signals while addressing the aforementioned limitations due tointerference.

These interferences take the following form when code divisionmultiplexing is employed for a communication link, either with codedivision multiple access (as used in CDMA 2000, WCDMA, and relatedstandards) or with time division multiple access (as used in EV-DO andrelated standards). A set of symbols is sent across a common timefrequency slot of the physical channel and separated using a set ofdistinct code waveforms, which are usually chosen to be orthogonal (orpseudo-orthogonal for reverse link transmissions). The code waveformstypically vary in time, and these variations are introduced by apseudo-random spreading code (PN sequence). The wireless transmissionmedium is characterized by a time-varying multipath profile that causesmultiple time delayed replicas of the transmitted waveform to bereceived, each replica having a distinct amplitude and phase due to pathloss, absorption, and other propagation effects. As a result, thereceived code set is no longer orthogonal. The code space suffers fromintra-channel interference within a base station as well asinter-channel interference arising from transmissions in adjacent cells.

The most basic receiver architecture employed to combat these variouseffects is the well-known Rake receiver. The Rake receiver uses achannel-tracking algorithm to resolve the received signal energy ontovarious multipath delays. These delayed signals are then weighted by theassociated complex channel gains (which maybe normalized by path noisepowers) and summed to form a single resolved signal, which exploits someof the path diversity available from the multipath channel. It is wellknown that the Rake receiver suffers from a significant interferencefloor, which is due to both self-interference from the base station ofinterest (or base stations, when the mobile is in a soft-handoff basestation diversity mode) and multiple-access interference from all basestations in the coverage area. This interference limits the maximum datarates achievable by the mobiles within a cell and the number of mobilesthat can be supported in the cell.

Advanced receivers have been proposed to overcome the limitations of theRake receiver. The optimal multi-user detector (MUD) has the bestperformance, but is generally too computationally complex to implement.MUD complexity increases exponentially with respect to the total numberof active subchannels across the cell of interest and the interferingcells as well as the constellation size(s) of the subchannels. Thiscomplexity is so prohibitive that even efficient implementations basedon the Viterbi algorithm cannot make it manageable in current hardwarestructures. Another approach is a properly designed linear receiver,which in many channel scenarios, is able to retain much of the optimalMUD performance, but with a complexity that is polynomial in the numberof subchannels. The most common examples are the linear minimum meansquared error (LMMSE) receiver and the related decorrelating (orzero-forcing) receiver, which both require finding, or approximating,the inverse of a square matrix whose dimension is equal to the lesserbetween the number of active subchannels and the length (in samples) ofthe longest spreading code.

Complexity can still be prohibitive with these receivers, because such amatrix inverse needs to be calculated (or approximated) for each symbol.These receivers depend not only on the spectral characteristics of themultipath fading channel (which could be slowly time varying), but alsoon the time-varying spreading codes employed on the sub channels overeach symbol. Thus, these receivers vary at the symbol rate even if thechannel varies much more slowly.

An alternative approach currently under development for advancereceivers sidesteps the need to invert a matrix for each symbol. Itaccomplishes this by employing a PN-averaged LMMSE (PNA-LMMSE) receiverthat assumes the PN code is random and unknown at the receiver (at leastfor determining the correlation matrix). While this receiver isgenerally inferior to the LMMSE approach, it has the advantage of nothaving to be implemented directly, because it is amenable to adaptive(or partially adaptive) implementations. The advantages of an adaptiveimplementation over a direct implementation include reduced complexityand the fact that the additive noise power (i.e., background RFradiation specific to the link environment, noise in the receiver's RFfront end, and any processing noise such as noise due to quantizationand imperfect filtering) does not have to be estimated. However, theseadvantages incur the costs associated with adaptive filters (e.g.,performance and adaptation rate). Note that a direct implementationwithout knowledge of the noise power modifies the LMMSE and PNA-LMMSEreceivers into the corresponding decorrelating (or zero-forcing)receivers that arise from taking the background noise power to be zerowhen deriving the LMMSE and PNA-MMSE receivers.

Another method for further reducing complexity is to iterativelyapproximate the matrix-inverse functionality of the LMMSE receiverwithout explicitly calculating the inverse. Receivers of this typeemploy multistage interference cancellation. One particular type isknown as parallel interference cancellation (PIC), and is motivated bywell-known iterative techniques of quadratic minimization. In each stageof PIC, the data symbols of the subchannels are estimated. For eachsubchannel, an interference signal from the other subchannels issynthesized, followed by interference cancellation that subtracts thesynthesized interference from each sub channel. Theinterference-cancelled subchannels are then fed to a subsequent PICstage. Ideally, within just a few stages (i.e., before the complexitygrows too large), the performance rivals that of the full linearreceiver using a matrix inverse.

PIC can be implemented in various modes depending on what types ofsymbol estimates are used for interference cancellation. In asoft-cancellation mode, PIC does not exploit additional informationinherent in the finite size of user constellations. That is, estimatesof data symbols are not quantized to a constellation point whenconstructing interference signals. However, in some multiple-accessschemes, the user constellations may be known (e.g., in an EV-DO link orin a WCDMA link without HSDPA users) or determined through a modulationclassifier. In such cases, it is possible for PIC to be implemented in ahard-cancellation mode. That is, estimates of data symbols are quantizedto constellation points (i.e., hard decisions) when constructing theinterference signal.

In a mixed-cancellation mode, PIC employs a soft decision on each symbolwhose constellation is unknown, and either a soft or hard decision oneach symbol whose constellation is known, depending on how close thesoft estimate is to the hard decision. Such a mixed-decision PICtypically outperforms both the soft-decision PIC and the hard-decisionPIC. Moreover, it can also substantially outperform the optimal LMMSEreceiver and promises even greater performance gains over PNA-LMMSEapproaches currently under development for advanced receivers. Theperformance of soft-decision PIC is bounded by the optimal LMMSE.

SUMMARY OF THE INVENTION

In view of the foregoing background, embodiments of the presentinvention may provide a generalized interference-canceling receiver forcanceling intra-channel and inter-channel interference in coded,multiple-access, spread-spectrum transmissions that propagate throughfrequency-selective communication channels. Receiver embodiments mayemploy a designed and/or adapted soft-weighting subtractive cancellationwith a stabilizing step-size and a mixed-decision symbol estimator.Receiver embodiments may be designed, adapted, and implementedexplicitly in software or programmed hardware, or implicitly in standardRake-based hardware, either within the Rake (i.e., at the finger level)or outside the Rake (i.e., at the sub channel symbol level). Embodimentsof the invention may be employed in user equipment on the forward linkand/or in a base station on the reverse link.

Some embodiments of the invention address the complexity of the LMMSEapproach by using a low-complexity iterative algorithm. Some embodimentsof the invention in soft-mode may be configured to achieve LMMSEperformance (as contrasted to the lesser-performing PNA-LMMSE) usingonly quantities that are easily measured at the receiver. Someembodiments address the sub-optimality of the LMMSE and PNA-LMMSEapproaches by using an appropriately designed mixed-decision mode andmay even approach the performance of an optimal multi-user detector. Insome embodiments, stabilizing step sizes may be used to enhancestability of various PIC approaches. Some embodiments may employsymbol-estimate weighting to control convergence of various PICapproaches. Some embodiments of the invention address the limitation ofvarious PIC approaches to binary and quaternary phase shift keying inmixed-decision mode by being configurable to any sub channelconstellation. Some embodiments of the invention address the difficultyof efficiently implementing various PIC approaches in hardware by usinga modified Rake architecture. Some embodiments of the invention addressthe so-called “ping-pong effect” (i.e., when the symbol error rateoscillates with iteration) in various PIC approaches by pre-processingwith a de-biasing operation when making symbol estimates.

In one embodiment of the invention, an iterative interference cancellercomprises a weighting means configured for applying at least one symbolweight to the input symbol decisions, a stabilizing step size meansconfigured for applying a stabilizing step size to an error signal, anda mixed-decision processing means. The canceller is iterative, and thus,the weighting means, the stabilizing step size means, and themixed-decision processing means are configured to perform processingduring each of a plurality of iterations for each of the input symboldecisions.

The mixed-decision processing means may include, by way of example, butwithout limitation, a combination of hardware and software configured toproduce soft and/or hard symbol estimates. The mixed-decision meanscomprises a de-biasing means configured for scaling the input symbolestimates with a scale factor to remove bias computed on the inputsymbol estimates, and a processing means configured for processing eachde-biased input symbol estimate, irrespective of other symbol estimates.The processing means produces a hard decision that quantizes thede-biased input symbol estimate onto a nearby constellation point, or asoft decision that scales the de-biased input symbol estimate.

The stabilizing step size means may include, by way of example, butwithout limitation, any combination of hardware and software configuredto scale an error signal with a scaling factor that may be used forcontrolling convergence in an iterative canceller. For example, thestabilizing step size means may include a signal processor configured tocalculate at least one stabilizing step size and a multiplier forscaling an error signal with the step size. The multiplier means isconfigured for scaling (e.g., multiplying) an error signal with thestabilizing step size.

The step size calculation means may include, by way of example, butwithout limitation, software or programmable hardware configured forcalculating a stabilizing step size.

The weighting means may include, by way of example, but withoutlimitation, a weight-calculation means configured for producing symbolweights, and a multiplier configured for scaling symbol estimates by theweights.

Embodiments of the invention may be employed in any receiver configuredto support one or more CDMA standards, such as (1) the “TIA/EIA-95-BMobile Station Base Station Compatibility Standard for Dual-ModeWideband Spread Spectrum Cellular System” (the IS-95 standard), (2) the“TIA/EIA-98-C Recommended Minimum Standard for Dual-Mode Wideband SpreadSpectrum Cellular Mobile Station” (the IS-98 standard), (3) the standardoffered by a consortium named “3rd Generation Partnership Project”(3GPP) and embodied in a set of documents including Document Nos. 3G TS25.211, 3G TS 25.212, 3G TS 25.213, and 3G TS 25.214 (the WCDMAstandard), (4) the standard offered by a consortium named “3rdGeneration Partnership Project 2” (3GPP2) and embodied in a set ofdocuments including “TR-45.5 Physical Layer Standard for cdma2000 SpreadSpectrum Systems,” the “C.S0005-A Upper Layer (Layer 3) SignalingStandard for cdma2000 Spread Spectrum Systems,” and the “C.S0024CDMA2000 High Rate Packet Data Air Interface Specification” (theCDMA2000 standard), (5) Multi-Code CDMA systems, such asHigh-Speed-Downlink-Packet-Access (HSDPA), and (6) other CDMA standards.

Receivers and cancellation systems described herein may be employed insubscriber-side devices (e.g., cellular handsets, wireless modems, andconsumer premises equipment) and/or server-side devices (e.g., cellularbase stations, wireless access points, wireless routers, wirelessrelays, and repeaters). Chipsets for subscriber-side and/or server-sidedevices may be configured to perform at least some of the receiverand/or cancellation functionality of the embodiments described herein.

These and other embodiments of the invention are described with respectto the figures and the following description of the preferredembodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments according to the present invention are understood withreference to the schematic block diagrams of FIGS. 1 through 12.

FIG. 1 is a general schematic illustrating an iterative interferencecanceller.

FIG. 2 is a block diagram illustrating a front-end processor for aniterative interference canceller.

FIG. 3 is a general schematic illustrating an interference cancellationunit (ICU).

FIG. 4 shows a weighting block in an ICU configured to separatelyprocess input symbol estimates corresponding to a plurality of basestations.

FIG. 5A is a block diagram illustrating part of an interferencecancellation unit configured to synthesize constituent finger signals.

FIG. 5B is a block diagram illustrating part of an interferencecancellation unit configured to synthesize constituent user signals.

FIG. 6A shows a cancellation block configured to perform interferencecancellation on constituent signals, followed by Rake processing anddespreading.

FIG. 6B shows a cancellation block configured to cancel interference inconstituent signals, preceded by Rake processing and despreading.

FIG. 7 is a block diagram of the interference cancellation part of asubtractive canceller in which cancellation occurs prior to signaldespreading.

FIG. 8A is a block diagram illustrating post interference-cancellationsignal despreading on constituent finger signals.

FIG. 8B is a block diagram illustrating post interference-cancellationsignal de spreading on constituent user signals.

FIG. 9A is a block diagram showing a method for implicitly despreading asignal in a subtractive canceller that performsinterference-cancellation prior to signal despreading.

FIG. 9B is a block diagram showing a method for explicitly despreading asignal in a subtractive canceller that performsinterference-cancellation prior to signal despreading.

FIG. 10 is a block diagram of a subtractive canceller configured toperform interference cancellation prior to signal despreading.

FIG. 11A is a block diagram illustrating an embodiment for implicitlycalculating a stabilizing step size.

FIG. 11B is a block diagram illustrating how linear functions, such asde spreading and generating a difference signal, can be swapped in analternative embodiment for calculating a stabilizing step size.

FIG. 11C is a block diagram illustrating another embodiment forimplicitly calculating a stabilizing step size.

FIG. 12 is a block diagram of a symbol-estimation block in aninterference cancellation unit.

FIG. 13 is a block diagram of a dual feedback algorithm configured forimplementing an iterative interference canceller.

Various functional elements or steps, separately or in combination,depicted in the figures may take the form of a microprocessor, digitalsignal processor, application specific integrated circuit, fieldprogrammable gate array, or other logic circuitry programmed orotherwise configured to operate as described herein. Accordingly,embodiments may take the form of programmable features executed by acommon processor or discrete hardware unit.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

First the invention will be described as it applies to a forward-linkchannel, and then extended to include reverse-link channels. Thefollowing formula represents an analog baseband signal received at amobile from multiple base stations, each with its own multipath channel,

          Equation  1${{y(t)} = {{\sum\limits_{s = 0}^{B - 1}{\sum\limits_{l = 0}^{L_{(s)}}{\alpha_{{(s)},l}{\sum\limits_{k = 0}^{K_{(s)} - 1}{b_{{(s)},k}{u_{{(s)},k}\left( {t - \tau_{{(s)},l}} \right)}}}}}} + {w(t)}}},{t \in \left( {o,T} \right)},$

with the following definitions

-   -   (0,T) is the symbol interval;    -   B is the number of modeled base stations and is indexed by the        subscript (s) which ranges from (0) to (B−1); here, and in the        sequel, the term “base stations” will be employed loosely to        include cells or sectors;    -   L_((s)) is the number of resolvable (or modeled) paths from base        station (s) to the mobile;    -   a_((s), l) and τ_((s), l) are the complex gain and delay,        respectively, associated with the l-th path of base station (s);    -   K_((s)) is the number of active users or subchannels in base        station (s) that share a channel via code-division multiplexing;        these users or subchannels are indexed from 0 to K_((s))−1;    -   u_((s), k)(t) is a code waveform (e.g., spreading waveform) of        base station (s) used to carry the k^(th) user's symbol for that        base station (e.g., a chip waveform modulated by a user-specific        Walsh code and covered with a base-station specific PN cover);    -   b_((s), k) is a complex symbol transmitted for the k^(th) user        or subchannel of base station (s);    -   and w(t) is zero-mean complex additive noise that contains both        thermal noise and any interference whose structure is not        explicitly modeled (e.g., inter-channel interference from        unmodeled base stations and/or intra-channel interference from        unmodeled paths).

Typically, a user terminal (e.g., a handset) is configured to detectonly symbols transmitted from its serving base station (e.g., thesymbols from base station (0)) or a subset thereof (e.g., symbols forthe k^(th) user of base station (0)). Interference can impede thedetermination of b_((s), k) from y(t). Not only is additive noise w(t)present, but there may be intra-channel and inter-channel interference.

Intra-channel interference typically occurs when multiple users areserved by a given base station (i.e., a serving base station). Even ifthe users' transmitted code waveforms are orthogonal, multipath in thetransmission channel causes the codes to lose their orthogonality.Inter-channel interference is caused by transmissions from non-servingbase stations whose signals contribute to the received baseband signaly(t).

FIG. 1 is a block diagram of an iterative interference canceller (IIC),which is a low-complexity receiver configured to mitigate intra-channeland inter-channel interference. The received baseband signal y(t) isinput to a front-end processor 101, which produces initial symbolestimates for all symbols of the active users served by at least onebase station. The initial symbol estimates are coupled to a firstinterference cancellation unit (ICU) 102 configured to cancel a portionof the intra-channel and inter-channel interference that corrupts thesymbol estimates. The ICU 102 outputs a first set of updated symbolestimates, which are interference-cancelled symbol estimates. Theupdated symbol estimates are coupled to a second ICU 103. A plurality Mof ICUs 102-104 illustrate an iterative process for performinginterference cancellation in which the initial symbol estimates areupdated M times.

FIG. 2 is a block diagram of the front-end processor 101 shown inFIG. 1. Each of a plurality B of Rake-based receiver components 201-203provides estimates of symbols transmitted from a corresponding basestation. The detailed block diagram depicted in Rake receiver 202represents the functionality of each of the components 201-203. Rakereceiver 202, corresponding to an s^(th) base station 202, includes aplurality L_((s)) of delay elements 210-211 configured to advance thereceived baseband signal y(t) in accordance with multipath-delayquantities {τ_((s), l)}_(l=0) ^(L) ^((s)) ⁻¹. The advanced signals arescaled 212-213 by corresponding path gains {α_((s), l)}_(l=0) ^(L)^((s)) ⁻¹ prior to combining 214 to produce a combined signal of theform

${\sum\limits_{l = 0}^{L_{(s)} - 1}{\frac{\alpha_{{(s)},l}^{*}}{{\underset{\_}{\alpha}}_{(s)}}{y\left( {t + \tau_{{(s)},l}} \right)}}},$

where

${{\underset{\_}{\alpha}}_{(s)}} = \left( {\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}}^{2}} \right)^{1/2}$

is the Euclidean norm of the path-gain vector, α _((s))=└α_((s), 0)α_((s), 1) . . . α_((s), L) _((s)) ⁻¹┘^(T), and the superscript Tdenotes the matrix transpose operator.

The combined signal is resolved onto the users' code waveforms bycorrelative despreading, which comprises multiplying 215-216 thecombined signals by complex conjugates of each code waveform, followedby integrating 217-218 the resultant products. A despread signalcorresponding to a k^(th) code waveform is

$\begin{matrix}{q_{{(s)},k} \equiv {\frac{1}{{{\underset{\_}{\alpha}}_{(s)}}^{2}}{\int\limits_{0}^{T}{{u_{{(s)},k}^{*}(t)}{\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}^{*}{y\left( {t + \tau_{{(s)},l}} \right)}{{t}.}}}}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

This value is also referred to as a Rake front-end soft estimate of thesymbol b_((s), k). Since Rake processing, combining, and despreading arelinear operations, their order may be interchanged. Thus, alternativeembodiments may be provided in which the order of the linear operationsis changed to produce q_((s), k).

A symbol estimator comprises scaling blocks 219-220 and function blocks221-222, which are configured to refine the estimates q_((s), k) intofront-end symbol estimates {circumflex over (b)}_((s), k) ^([0]) of thetransmitted data symbols b_((s), k). Each of the functions depicted inFIG. 2 may be configured to process discrete-time sequences. Forexample, time advances 210-211 (or delays) may be implemented as shiftsby an integer number of samples in discrete-time sequences, andintegration 217-218 may employ summation.

FIG. 3 is a block diagram of an i^(th) ICU comprising four functionalblocks. A weighting module 301 calculates and applies soft weights toinput symbol estimates. A synthesizing module 302 processes weightedsymbol estimates to synthesize constituent signals of an estimatedreceived signal. For example, the estimated received signal y(t) is asum of the constituent signals, each of which is synthesized from theweighted symbol estimates. The synthesized constituents are processed ina canceller 303 (such as a subtraction module) configured to produceinterference-cancelled signals having reduced intra-channel andinter-channel interferences. The canceller 303 also includes a resolvingmodule (not shown) configured to resolve the interference-cancelledsignals onto user code waveforms to produce resolved signals. Amixed-decision module 304 processes the resolved signals to produceupdated symbol estimates.

FIG. 4 shows a weighting module (such as weighting module 301)configured to separately process input symbol estimates corresponding toa plurality B of base stations. A plurality of scaling modules 401-403scale the input symbol estimates. Scaling module 402 depicts detailedfunctionality for processing signals from an exemplary base station.Similar details are typically present in each of the scaling modules401-403.

A plurality K_((s)) of symbol estimates {{circumflex over (b)}_((s), k)^([i])}_(k=0) ^(K) ^((s)) ⁻¹ of transmitted symbols {b_((s), k)}_(k=0)^(K) ^((s)) ⁻¹ produced by an i^(th) ICU is input to scaling module 402.The symbol estimates are multiplied 410-411 by corresponding complexweights {γ_((s), k) ^([i])}_(k=0) ^(K) ^((s)) ⁻¹ to produce weightedsymbol estimates {γ_((s), k) ^([i]){circumflex over (b)}_((s), k)^([i])}_(k=0) ^(K) ^((s)) ⁻¹. The magnitude of weight γ_((s), k) ^([i])may be calculated with respect to a merit of the corresponding symbolestimate {circumflex over (b)}_((s), k) ^([i]).

The soft weights can be regarded as a confidence measure related to theaccuracy of a decision, or symbol estimate. For example, a highconfidence weight relates to a high certainty that a correspondingdecision is accurate. A low confidence weight relates to a lowcertainty. Since the soft weights are used to scale decisions,low-valued weights reduce possible errors that may be introduced into acalculation that relies on symbol estimates.

In one embodiment of the invention, the weights γ_((s), k) ^([i]) may bederived from at least one signal measurement, such as SINR. Clearly, thelarger the SINR, the greater the reliability of the corresponding symbolestimate. For example, the weights γ_((s), k) ^([i]) may be expressed by

$\begin{matrix}{{\gamma_{{(s)},k}^{\lbrack \rbrack} = {\max \left\{ {C_{{(s)},k},\frac{1}{1 + {1/{SINR}_{{(s)},k}^{\lbrack \rbrack}}}} \right\}}},} & {{Equation}\mspace{14mu} 3}\end{matrix}$

where SINR_((s), k) ^([i]) is a ratio of average signal power tointerference-plus-noise power of a k^(th) user in base station CO afterthe i^(th) ICU, and C_(k) is a non-negative real constant that can beused to ensure some feedback of a symbol estimate, even if its SINR issmall. Note that, as the SINR grows large, the weight tends towardunity, meaning that the estimate is very reliable.

The SINR (and thus, the soft weights) may be evaluated using techniquesof statistical signal processing, including techniques based on anerror-vector magnitude (EVM). Alternatively, a pilot-assisted estimateof the broadband interference-plus-noise floor, together with a userspecific estimate of the signal-plus-interference-plus-noise floor, maybe used to estimate the SINR values.

In another embodiment of the invention, the weights γ_((s), k) ^([i])may be expressed as a function of symbol estimates {circumflex over(b)}_((s), k) ^([i]) such as shown in the following equation

$\begin{matrix}{{\gamma_{{(s)},k}^{\lbrack \rbrack} = \frac{{Re}\left\{ {E\left\lbrack {{{slice}\left( {\hat{b}}_{{(s)},k}^{\lbrack \rbrack} \right)}*{\hat{b}}_{{(s)},k}^{\lbrack \rbrack}} \right\rbrack} \right\}}{E\left\lbrack {{\hat{b}}_{{(s)},k}^{\lbrack \rbrack}}^{2} \right\rbrack}},} & {{Equation}\mspace{14mu} 4}\end{matrix}$

where Re{ } returns the real part of the argument. The statisticalexpectations E[ ] in the numerator and denominator can be estimated, forexample, via time-series averaging. The term slice ({circumflex over(b)}_((s), k) ^([i])) represents the symbol estimate sliced {circumflexover (b)}_((s), k) ^([i]) (i.e., quantized), to the nearestconstellation point from which the symbol b_((s), k) was drawn. Thisapproach is applicable for symbols with known constellations. Forexample, it is typical for a receiver to know the symbol constellationfor a user of interest, but it may not know which constellations areassigned to other users.

In this embodiment, the weights γ_((s), k) ^([i]) are a function of asymbol estimate's {circumflex over (b)}_((s), k) ^([i]) proximity to agiven constellation point. Thus, a symbol estimate {circumflex over(b)}_((s), k) ^([i]) that is close to a constellation point is providedwith a large weight indicative of a high confidence measure in thesymbol estimate's accuracy. For example, if the value {circumflex over(b)}_((s), k) ^([i]) is a hard-decision estimate of b_((s),k) (i.e., itis quantized to the nearest constellation point), then its associatedweight is γ_((s), k) ^([i])=1, which indicates a high degree ofconfidence in the symbol estimate.

In some embodiments, both Equation 3 and Equation 4 may be used in areceiver to calculate soft weights. Some embodiments of the inventionmay provide for subset selection to force one or more of the weights tozero. Such embodiments may be expressed as adaptations to Equation 3and/or Equation 4 expressed by

γ_((s),k) ^([i])=0 for some subset of the users.  Equation 5

Forcing the weights of some users to zero effectively restricts whichuser signals are employed for interference cancellation. Someembodiments may provide for canceling only a predetermined number P ofstrongest users (e.g., users having the largest weight values). Thenumber P may be fixed for all iterations, or it may vary with respect toiteration. In some embodiments, the number P may range from zero (i.e.,no interference cancellation) to

$K = {\sum\limits_{s = 0}^{B - 1}K_{(s)}}$

(i.e., interference of all users cancelled). In some embodiments, theweights of user signals transmitted from at least one weakest basestation are set to zero.

FIG. 5A is a block diagram of a synthesizing module (such as thesynthesizing module 302) in which the constituent signals are associatedwith each Rake finger. Each of a plurality B of synthesizing modules501-503 is assigned to one of a plurality B of base stations. A blockdiagram for an exemplary synthesizing module 502 corresponding to a basestation (s) depicts details that are common to all of the synthesizingmodules 501-503.

Weighted symbol estimates γ_((s), k) ^([i]){circumflex over(b)}_((s), k) are modulated 510-511 onto corresponding code waveformsu_((s), k)(t) to produce a plurality K_((s)) of coded waveforms, whichare combined in combining module 512 to produce a synthesizedtransmission signal

$\sum\limits_{k = 0}^{K_{(S)} - 1}{\gamma_{{(s)},k}^{\lbrack \rbrack}{\hat{b}}_{{(s)},k}^{\lbrack \rbrack}{{u_{{(s)},k}(t)}.}}$

Channel emulation (including delaying the synthesized transmissionsignal 513-514 τ_((s), l) by and scaling 515-516 with channel gainsα_((s), l)) is performed to produce constituent signals corresponding toeach finger. A synthesized constituent signal for an i^(th) finger ofbase station (s) is

$\begin{matrix}{{\overset{\sim}{y}}_{{(s)},l}^{\lbrack \rbrack} \equiv {\alpha_{{(s)},l}{\sum\limits_{k = 0}^{K_{(s)} - 1}{\gamma_{{(s)},k}^{\lbrack \rbrack}{\hat{b}}_{{(s)},k}^{\lbrack \rbrack}{{u_{{(s)},k}\left( {t - \tau_{{(s)},l}} \right)}.}}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

When all of the finger constituents are summed, the result is

$\begin{matrix}{{{{\overset{\sim}{y}}^{i\rbrack}(t)} = {\sum\limits_{s = 0}^{B - 1}{\sum\limits_{l = 0}^{L_{(s)} - 1}{{\overset{\sim}{y}}_{{(s)},l}^{\lbrack i\rbrack}(t)}}}},} & {{Equation}\mspace{14mu} 7}\end{matrix}$

which is an estimate of the signal that would be received at the mobileif the base stations were to transmit the weighted symbols.

FIG. 5B is a block diagram of a synthesizing module (such as thesynthesizing module 302) in which the constituent signals are associatedwith each user in the system. Each synthesizing module 521-523 isconfigured to emulate multipath channels for all base stations.Synthesizing module 522 includes a block diagram that is indicative ofthe functionality of each of the synthesizing module 521-523. Insynthesizing module 522, a plurality K_((s)) of modulators (such asmodulator 531) modulates each weighted symbol γ_((s), k)^([i]){circumflex over (b)}_((s), k) onto a corresponding code waveformu_((s), k)(t). Each modulated code waveform is processed by a bank offinger delay elements 532-533 and channel gain scaling elements 534-535corresponding to the multipath channel of base station (s). Theresulting emulated multipath components are combined in combining module536 to produce an estimated received signal for a k^(th) user of basestation (s),

$\begin{matrix}{{\overset{\sim}{y}}_{{(s)},l}^{\lbrack i\rbrack} \equiv {\alpha_{{(s)},l}{\sum\limits_{k = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}\gamma_{{(s)},k}^{\lbrack i\rbrack}{\hat{b}}_{{(s)},k}^{\lbrack i\rbrack}{{u_{{(s)},k}\left( {t - \tau_{{(s)},l}} \right)}.}}}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

The subscript k on the left-hand side denotes that the constituentsignal is for a user k, whereas the subscript l on the left-hand side ofEquation 6 represents that the constituent signal is for a finger l. Thesum of the user constituent signals produces a synthesized receivedsignal

$\begin{matrix}{{{\overset{\sim}{y}}^{\lbrack i\rbrack}(t)} = {\sum\limits_{s = 0}^{B - 1}{\sum\limits_{k = 0}^{K_{(s)} - 1}{{{\overset{\sim}{y}}_{{(s)},k}^{\lbrack i\rbrack}(t)}.}}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

The left-hand sides of Equation 7 and Equation 9 are the same signal,whereas the right-hand sides are simply two different decompositions.

FIG. 6A shows a cancellation module 601 (such as the canceller 303 inFIG. 3) configured to perform interference cancellation 610 onconstituent signals, followed by Rake processing and de spreading 611 ina Rake-based receiver. FIG. 6B shows a cancellation module 602configured to synthesize 621 a received signal from constituentcomponents, followed by Rake processing and despreading 622, andinterference cancellation 623.

FIG. 7 is a block diagram of an interference canceller comprising aplurality B of cancellers 701-703 configured to perform interferencecancellation on a plurality J of constituent signals for each of aplurality B of base stations. Since the constituent signals may beeither fingers or users, index jε{0, 1, . . . , J_((s))−1} is expressedby

$J_{(s)} = \left\{ {\begin{matrix}{L_{(s)}\mspace{14mu} {for}\mspace{14mu} {finger}\mspace{14mu} {constituents}} \\{K_{(s)}\mspace{14mu} {for}\mspace{14mu} {user}\mspace{14mu} {constituents}}\end{matrix}.} \right.$

Canceller 702 includes a block diagram that represents the functionalityof each of the cancellers 701-703. The constituent signals correspondingto each base station are summed in a combining module 711 to produce asynthesized received signal,

${{\overset{\sim}{y}}_{(s)}^{\lbrack i\rbrack} \equiv {\sum\limits_{j = 0}^{J_{(s)} - 1}{\overset{\sim}{y}}_{{(s)},j}^{\lbrack i\rbrack}}},\mspace{14mu} {{where}\mspace{14mu} {\overset{\sim}{y}}_{{(s)},j}^{\lbrack i\rbrack}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} j^{th}}$

constituent signal (either finger or user) for base station (s). Aplurality B of these sums corresponding to different base stations arecombined in combining module 721 to produce a synthesized receive signal

${{\overset{\sim}{y}}^{\lbrack i\rbrack}(t)} = {\sum\limits_{s = 0}^{B - 1}{{{\overset{\sim}{y}}_{(s)}^{\lbrack i\rbrack}(t)}.}}$

The synthesized receive signal is subtracted from the actual receivedsignal in a subtraction module 722 to produce a residual signaly(t)−{tilde over (y)}^([i])(t). A stabilizing step size module 723scales the residual signal by a complex stabilizing step size μ^([i]) toproduce a scaled residual signal μ^([i])(y(t)−{tilde over(y)}^([i])(t)). The scaled residual signal is combined with theconstituent signals {tilde over (y)}_((s),j) ^([i]) in combining modules712-714 to produce a set of interference-cancelled constituentsrepresented by

z _((s),j) ^([i])(t)≡{tilde over (y)} _((s),j)^([i])(t)+μ^([i])(y(t)−{tilde over (y)} ^([i])(t)),  Equation 10

where z_((s),j) ^([i])(t) is an interference-cancelled j^(th)constituent signal for base station (s).

In an alternative embodiment, cancellation may be performed with only asubset of the constituent channels. In each base station, only thoseconstituent signals being used for cancellation may be used tosynthesize the estimated receive signal for base station (s). Thus{tilde over (y)}_((s)) ^([i]) becomes

${\overset{\sim}{y}}_{(s)}^{\lbrack i\rbrack} \equiv {\sum\limits_{j \in J_{(s)}}{\overset{\sim}{y}}_{{(s)},j}^{\lbrack i\rbrack}}$

where J_((s)) ⊂{0, 1, . . . , J_((s))−1} are indices of the subset ofconstituent signals to be employed in cancellation. Embodiments of theinvention may be configured for applications in which hardwarelimitations restrict the number of finger signals or user signals thatcan be used for interference cancellation (e.g., only the strongestconstituents are used).

The interference-cancelled signals produced by the canceller shown inFIG. 7 may be processed by a Rake despreader shown in FIG. 8A, which isconfigured for processing finger inputs. Specifically, finger signalsassociated with each base station are input to corresponding fingers ofa Rake despreading module tuned to that base station. A Rake despreadingmodule 802 tuned to an s^(th) base station comprises a block diagramindicating the functionality of a plurality B of Rake despreadingmodules 801-803.

Interference cancelled signals z_((s), l) ^([i])(t) are time-advanced810-811 by an) amount τ_((s), l). A maximal ratio combining modulescales 812-813 each time-advanced signal z_((s), l) ^([i])(t+τ_((s), l))by α_((s), l)*/∥α _((s))∥ and combines 814 the time-advanced signals foreach base station. A resolving module comprising multipliers 815-816 andintegrators 817-818 resolves each combined signal

$\frac{1}{{\underset{\_}{\alpha}}_{(s)}}{\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}^{*}{z_{{(s)},l}^{\lbrack i\rbrack}\left( {t + \tau_{{(s)},l}} \right)}}}$

onto code waveforms associated with base station (s) via correlativedespreading. The resulting quantity for a k^(th) user of base station(s) is denoted by

$\begin{matrix}{{\overset{\sim}{b}}_{{(s)},k}^{\lbrack i\rbrack} \equiv {\frac{1}{{{\underset{\_}{\alpha}}_{(s)}}^{2}}{\int\limits_{0}^{T}{{u_{{(s)},k}^{*}(t)}{\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}^{*}{{z_{{(s)},l}^{\lbrack i\rbrack}\left( {t + \tau_{{(s)},l}} \right)}.}}}}}}} & {{Equation}\mspace{14mu} 11}\end{matrix}$

FIG. 8B is a block diagram of a Rake despreader configured forprocessing interference-cancelled signals relating to user inputs. Inthis case, the input constituent signals are user signals. A Rakedespreading module 822 tuned to an s^(th) base station comprises a blockdiagram indicating functional details that are common to a plurality Bof Rake despreading modules 821-823.

Interference cancelled signals z_((s), k) ^([i])(t) corresponding to ak^(th) user and s^(th) base station are processed by a plurality L_((s))of time-advance modules 831-832 corresponding to the multipath channelfor the s^(th) base station. The resulting time-advanced signals{z_((s), k) ^([i])(t+τ_((s), l))}_(l=0) ^(L) ^((s)) ⁻¹ are weighted by aplurality of weighting modules 833-834, and the weighted signals arecombined in combiner 835. A resolving module comprising multiplier 836and integrator 837 resolves the combined signal onto the k^(th) user'scode waveform to give

$\begin{matrix}{{\overset{\sim}{b}}_{{(s)},k}^{\lbrack i\rbrack} \equiv {\frac{1}{{{\underset{\_}{\alpha}}_{(s)}}^{2}}{\int\limits_{0}^{T}{{u_{{(s)},k}^{*}(t)}{\sum\limits_{l = 0}^{L_{(s)} - 1}{a_{{(s)},l}^{*}{{z_{{(s)},l}^{\lbrack i\rbrack}\left( {t + \tau_{{(s)},l}} \right)}.}}}}}}} & {{Equation}\mspace{14mu} 12}\end{matrix}$

The values of {tilde over (b)}_((s), k) ^([i]) shown in Equation 11 andEquation 12 are generally not the same value, since the value of {tildeover (b)}_((s), k) ^([i]) in Equation 11 is produced by cancellationemploying finger constituents, whereas {tilde over (b)}_((s), k) ^([i])expressed by Equation 12 is produced by cancellation employing userconstituents.

FIG. 9A is a block diagram of a Rake despreader, such as Rakedespreaders 611 and 622 shown in FIGS. 6A and 6B, respectively. The Rakedespreader comprises a plurality B of Rake despreading modules 901-903,each configured to process constituent signals from one of a plurality Bof base stations. An exemplary Rake despreader module 902 is a blockdiagram illustrating functionality of each of the Rake despreadermodules 901-903.

Input constituent signals {tilde over (y)}_((s), j) ^([i]) (t) for allvalues off are subtracted 911 from the received signal y(t) to produce adifference signal, or error signal, representing the difference betweenthe received signal and the synthesized estimates of signals received bythe base stations. For base station (s), the difference signal isy(t)−{tilde over (y)}_((s)) ^([i])(t), where

${{\overset{\sim}{y}}_{(s)}^{\lbrack i\rbrack}(t)} \equiv {\sum\limits_{j = 0}^{J_{(s)} - 1}{{{\overset{\sim}{y}}_{{(s)},j}^{\lbrack i\rbrack}(t)}.}}$

The difference signal for base station (s) is processed by a parallelbank of time advance modules 912-913 associated with the multipathchannel for that base station, followed by maximal-ratio combining. Inthis embodiment, a maximal-ratio combining module is configured toperform weighting 914-915 and combining 916. A resolving modulecomprising multipliers 917-918 and integrators 919-920 resolves theresulting combined signals onto code waveforms of the base station'susers to give the difference signal vector, or error signal vector,q_((s),k)−{tilde over (q)}_((s), k) ^([i]), where

$\begin{matrix}{{\overset{\sim}{q}}_{{(s)},k}^{\lbrack i\rbrack} = {\frac{1}{{{\underset{\_}{\alpha}}_{(s)}}^{2}}{\int\limits_{0}^{T}{{u_{{(s)},k}^{*}(t)}{\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}^{*}{z_{(s)}^{\lbrack i\rbrack}\left( {t + \tau_{{(s)},l}} \right)}}}}}}} & {{Equation}\mspace{14mu} 13}\end{matrix}$

and z_((s)) ^([i])(t) was defined in Equation 10.

Rake despreading, such as described with respect to the exemplary Rakedespreading module 902, may also be accomplished explicitly by employingmatrix multiplication to synthesize constituent signals of the receivedsignal, such as represented by block 931 shown in FIG. 9B.

A diagonal soft-weighting matrix may be defined as

Γ^([i])=diag(γ_((0),0) ^([i]), . . . , γ_((0),K) ₍₀₎ ⁻¹ ^([i])| . . .|γ_((B−1),0) ^([i]), . . . , γ_((B−1),K) _((B−1)) ⁻¹ ^([i])),  Equation14

in which all of the users' soft weights are ordered first by basestation and then by users within a base station. The same indexing mayalso be used to express the column vector of symbol estimates input toan i^(th) ICU as

{circumflex over (b)} ^([i]) =[{circumflex over (b)} _((0),0) ^([i]) , .. . , {circumflex over (b)} _((0),K) ₍₀₎ ⁻¹ ^([i]) | . . . |{circumflexover (b)} _((B−1),0) ^([i]) , . . . , {circumflex over (b)} _((B−1),K)_((B−1)) ⁻¹ ^([i])]^(T).  Equation 15

The weighted symbol estimates are expressed as Γ^([i]) b ^([i]), and theoutputs of the Rake despreading modules 901-903 are expressed by thedifference equation,

q−{tilde over (q)} ^([i]) =q−RΓ ^([i]) b ^([i]),  Equation 16

where,

q=[q _((0),0) , . . . , q _((0),K) ₍₀₎ ⁻¹ | . . . |q _((B−1),0) , . . ., q _((B−1),K) _((B−1)) ⁻¹]^(T)  Equation 17

{tilde over (q)} ^([i]) =[{tilde over (q)} _((0),0) ^([i]) , . . . ,{tilde over (q)} _((0),K) ₍₀₎ ⁻¹ ^([i]) | . . . |{tilde over (q)}_((B−1),0) ^([i]) , . . . , {tilde over (q)} _((B−1),K) _((B−1)) ⁻¹^([i])]^(T).  Equation 18

The values of q ^([i]) represent the despread signals, such as describedwith respect to FIG. 2. The values of {tilde over (q)} ^([i]) arerepresented by Equation 13, and R is a square matrix whose elements arecorrelations between the users' received code waveforms. In FIG. 9B, thefunctionality expressed by Equation 16 is implemented via thematrix-multiplication block 931 and a subtraction module 932.

A global index

$\kappa \in {{\left\{ {0,1,\ldots \mspace{14mu},{K - 1}} \right\} \mspace{14mu} {with}\mspace{14mu} K} \equiv {\sum\limits_{s = 0}^{B - 1}K_{(s)}}}$

is employed for ordering users (first by base station, and then by userswithin a base station) described with respect to Equation 14. Thus, κ=0corresponds to a first user (denoted by index zero) of a first basestation (denoted by an index zero), and κ=K corresponds to a last user(denoted by index K_((B−1))−1) of a last base station (denoted by index(B−1). If a user κ is a member of base station (s) and a user κ′ is amember of base station (s′), then the (κ, κ′) element of matrix R may beexpressed by

$\begin{matrix}{R_{{\kappa\kappa}^{\prime}} = {\frac{1}{\begin{matrix}{{\underset{\_}{\alpha}}_{(s)}} \\{{\underset{\_}{\alpha}}_{(s^{\prime})}}\end{matrix}}{\int\begin{matrix}{\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}{u_{\kappa}\left( {t - \tau_{{(s)},l}} \right)}}} \\{\sum\limits_{l^{\prime} = 0}^{L_{(s^{\prime})} - 1}{\alpha_{{(s^{\prime})},l^{\prime}}^{*}{u_{\kappa^{\prime}}^{*}\left( {t - \tau_{{(s^{\prime})},l^{\prime}}} \right)}{{t}.}}}\end{matrix}}}} & {{Equation}\mspace{14mu} 19}\end{matrix}$

Thus, the elements of R can be built at the receiver with estimates ofthe path gains, path delays, and knowledge of the users' code waveforms.

FIG. 10 is a block diagram of an interference canceller, such as theinterference-cancellation block 623 shown in FIG. 6. The differencesignal q_((s), k)−{tilde over (q)}_((s), k) ^([i]) is scaled with astabilizing step size μ^((i)) by a stabilizing step size module 1001,which may include a calculation module (not shown) configured tocalculate a stabilizing step size having a magnitude that is a functionof proximity of input symbol decisions to a desiredinterference-cancelled symbol decision. The resulting scaled differencesignal is summed 1003 with a product 1002 of the weighted symbolestimates Γ^([i]) {circumflex over (b)} ^([i]) and a K×K implementationmatrix F to yield

{tilde over (b)} ^([i+1]) =FΓ ^([i]) {circumflex over (b)}^([i])+μ^([i])( q−{tilde over (q)} ^([i])).  Equation 20

The choice of F allows interference cancellation after despreading tomimic interference cancellation prior to despreading for either userconstituents or finger constituents. For user constituents, F=I. Forfinger constituents, F is a block-diagonal matrix with a plurality B ofdiagonal blocks, wherein an s^(th) diagonal block is a K_((s))×K_((s))block representing the users' transmit correlation matrix for basestation (s). The (k, k′) element of the s^(th) diagonal block (denotedby F_((s)(s))) is equal to

(F _((s)(s)))_(kk′) =∫u _((s),k)(t)u _((s),k′)*(t)dt.  Equation 21

The stabilizing step size μ^([i]) may be used to enhance interferencecancellation in each ICU and/or stabilize iterative interferencecancellation. A quality metric of a canceller's output {tilde over (b)}^([i+1]) may be derived as follows. If it is known (or approximated)that the additive noise w(t) in Equation 1 is Gaussian, then thedespread outputs q, conditional on the transmitted symbols

b=[b _((0),0) , . . . , b _((0),K) ₍₀₎ ⁻¹ | . . . |b _((B−1),0) , . . ., b _((B−1),K) _((B−1)) ⁻¹]^(T),  Equation 22

are jointly complex normal random variables with mean Rb and covarianceΓ^([i])R (i.e., q|b is distributed as CN(Rb;R)). If it is approximatedthat q|{tilde over (b)} ^([i+1]) is distributed as CN(R{tilde over (b)}^([i+1]); R), where {tilde over (b)} ^([i+1]) and its dependence onμ^([i]) are given by Equation 20, then the value of μ^(th) that givesthe maximum-likelihood soft estimate for {tilde over (b)} ^([i+1]) is

$\begin{matrix}{\mu^{\lbrack i\rbrack} = {\frac{\left( {\underset{\_}{q} - {{RF}\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}{\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}{R\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}}.}} & {{Equation}\mspace{14mu} 23}\end{matrix}$

Alternatively, the value of μ^([i]) that gives the maximum-likelihoodsoft estimate for Γ^([i]) {tilde over (b)} ^([i+1]) is

$\begin{matrix}{\mu^{\lbrack i\rbrack} = \frac{\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}F\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}{\Gamma^{\lbrack i\rbrack}\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}}{\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}\left( \Gamma^{\lbrack i\rbrack} \right)^{H}R\; {\Gamma^{\lbrack i\rbrack}\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}}} & {{Equation}\mspace{14mu} 24}\end{matrix}$

Different formulations of the step-size may be used within the same IIC.For example, a step size based on Equation 24 maybe used in a sequenceof ICUs and Equation 23 may be used in the last ICU of the sequence. TheEquations 23 and 24 may be adapted for cases in which there is no softweighting (i.e., when Γ^([i])=I). Similarly step-size equations may beadapted when constituent user signals are employed (i.e., F=I).Furthermore, Equation 23 and Equation 24 may be determined implicitlywhenever F=I, or when F is approximated as I. Since F contains theusers' correlation matrices at the transmitter for each base station asits block diagonal, it will approximately equal identity, as the users'code waveforms are typically designed to be mutually orthogonal (orquasi-orthogonal for the reverse link). Any non-orthogonality is due tothe finite duration of the pulse-shaping filters that approximate theirinfinite-duration theoretical counterparts. In this case, Equation 23becomes

$\begin{matrix}{\mu^{\lbrack i\rbrack} = \frac{\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}{\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}{R\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}}} & {{Equation}\mspace{14mu} 25}\end{matrix}$

FIG. 11A illustrates a method and apparatus for calculating astabilizing step size. A Rake receiver 1100 comprises a first Rake,maximal ratio combining, and despreading unit 1101 to process a receivedsignal y(t) for producing an output despread signal vector q. A secondRake, maximal ratio combiner, and despreader unit 1102 processes asynthesized receive signal with weighted symbol estimates correspondingto an i^(th) iteration, and represented by

${{{\overset{\sim}{y}}^{\lbrack i\rbrack}(t)} = {\sum\limits_{s = 0}^{B - 1}{\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}{\sum\limits_{k = 0}^{K_{(s)} - 1}{\gamma_{{(s)},k}^{\lbrack i\rbrack}{\hat{b}}_{{(s)},k}^{\lbrack i\rbrack}{u_{{(s)},k}\left( {t - \tau_{{(s)},l}} \right)}}}}}}},$

to produce an estimated received signal RΓ^([i]) {circumflex over (b)}^([i]).

A combiner 1103 calculates the difference between the outputs of 1101and 1102 to produce a difference signal, or error signal, β^([i])≡q−RΓ^([i]) {circumflex over (b)} ^([i]), whose elements areindexed first by base station, and then by users within a base station,

β ^([i])=[β_((0),0) ^([i]), . . . , β_((0),K) ₍₀₎ ⁻¹ ^([i])| . . .|β_((B−1),0) ^([i]), . . . , β_((B−1),K) _((B−1)) ⁻¹ ^([i])]^(T).

Alternatively, since the operations used to produce β^([i]) are linear,a difference signal y(t)−{tilde over (y)}_((s)) ^([i])(t) may beproduced prior to despreading, such as shown by block 1110 in FIG. 11B.

The norm-square of β ^([i]) is (i.e., ∥β ^([i])∥²) is evaluated 1104 togenerate the numerator in Equation 25. The elements of β ^([i]) areprocessed 1105 to produce a synthesized received signal

${\sum\limits_{s = 0}^{B - 1}{\sum\limits_{l = 0}^{L - 1}{\alpha_{{(s)},l}{\sum\limits_{k = 0}^{K - 1}{\beta_{{(s)},k}^{\lbrack i\rbrack}{u_{{(s)},k}\left( {t - \tau_{{(s)},l}} \right)}}}}}},$

and the norm square of this signal is calculated 1106 to produce thedenominator of Equation 25.

FIG. 11C is a block diagram of a method and apparatus for implicitlycalculating a stabilizing step size for the special case of F=I. In thiscase, Equation 24 becomes

$\begin{matrix}{{\mu^{\lbrack i\rbrack} = \frac{\left( {\underset{\_}{q} - {{R\left( \Gamma^{\lbrack i\rbrack} \right)}^{2}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}{\Gamma^{\lbrack i\rbrack}\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}}{\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)^{H}\left( \Gamma^{\lbrack i\rbrack} \right)^{H}R\; {\Gamma^{\lbrack i\rbrack}\left( {\underset{\_}{q} - {R\; \Gamma^{\lbrack i\rbrack}{\hat{\underset{\_}{b}}}^{\lbrack i\rbrack}}} \right)}}},} & {{Equation}\mspace{14mu} 26}\end{matrix}$

The signal β ^([i]) is generated by a Rake, maximal ratio combining, anddespreading unit 1120 and multiplied 1121 by Γ^([i]) to produce vectorΓ^([i]) β ^([i]). A synthesis module 1122 processes the vector Γ^([i]) β^([i]) to produce a synthesized receive vector, which is norm-squared1123 to produce the denominator of Equation 26.

A synthesized received signal is generated 1124 from the vector(Γ^([i]))² β ^([i]) and processed with received signal y(t) by an adder1125 to produce a difference signal. A Rake/combiner/despreader 1126processes the difference signal to generate the vector q−R(Γ^([i]))²{circumflex over (b)} ^([i]). The inner product 1127 between this vectorand the vector Γ^([i]) β ^([i]) gives the numerator of Equation 26.

In an alternative embodiment, the stabilizing step size may be derivedfrom the multipath channel gains,

$\begin{matrix}{{\mu^{\lbrack i\rbrack} = {\mu = {\max \left\{ {C,\left( \frac{\max\limits_{{(s)},l}{\alpha_{{(s)},l}}^{p}}{\sum\limits_{s = 0}^{B - 1}{\sum\limits_{l = 0}^{L_{(s)} - 1}{\alpha_{{(s)},l}}^{p}}} \right)^{r}} \right\}}}},} & {{Equation}\mspace{14mu} 27}\end{matrix}$

where μ^([i]) is fixed for every ICU and C, p and r are non-negativeconstants.

FIG. 12 is a block diagram of a symbol-estimation block comprising aplurality B of mixed-decision modules 1201-1203 configured to processsignals received from B base stations. Mixed-decision module 1202 showsfunctionality that is common to all of the mixed-decision modules1201-1203. De-biasing modules 1210-1211 scale each of a pluralityK_((s)) of input symbol estimates {tilde over (b)}_((s), k) ^([i+1])with a non-negative de-biasing constant d_((s), k) ^([i]) for producingde-biased input symbol estimates. The mixed-decision module 1202includes symbol-estimation modules 1212-1213 configured to performsymbol estimation on de-biased input symbol estimates whoseconstellations are known at the receiver.

The de-biasing constant may be expressed by

$\begin{matrix}{d_{{(s)},k}^{\lbrack i\rbrack} = {{E\left\lbrack {b_{{(s)},k}} \right\rbrack}/{E\left\lbrack {{\overset{\sim}{b}}_{{(s)},k}^{\lbrack{i + 1}\rbrack}} \right\rbrack}}} & {{Equation}\mspace{14mu} 28} \\{d_{{(s)},k}^{\lbrack i\rbrack} = \sqrt{{E\left\lbrack {b_{{(s)},k}}^{2} \right\rbrack}/{E\left\lbrack {{\overset{\sim}{b}}_{{(s)},k}^{\lbrack{i + 1}\rbrack}}^{2} \right\rbrack}}} & {{Equation}\mspace{14mu} 29} \\{{d_{{(s)},k}^{\lbrack i\rbrack} = {1\mspace{14mu} {if}\mspace{14mu} {the}\mspace{14mu} {symbol}\mspace{14mu} {constellation}\mspace{14mu} {is}\mspace{14mu} {unknown}}},} & {{Equation}\mspace{14mu} 30}\end{matrix}$

where the statistical expectations may be approximated bytime-averaging. De-biasing helps mitigate the “ping-pong” phenomenonoften associated with iterative interference cancellation in which thesymbol error rate oscillates with respect to iterations. Afterde-biasing, each valued d_((s), k) ^([i]){tilde over (b)}_((s),k)^([i+1]) is operated on by a map Φ_((s), k) that takes the input intothe complex plane to yield the updated symbol estimate

{circumflex over (b)} _((s),k) ^([i+1])=Ψ_((s),k)(d _((s),k) ^([i]){tilde over (b)} _((s),k) ^([i+1])).  Equation 31

The map Ψ_((s), k) may be a mixed-decision map, which is a combinationof soft and hard decisions. A soft-decision map is provided by afunction Ψ_((s), k)(x) that is a continuous function whose output rangesover the complex plane. Common examples, include, but are not limitedto,

$\begin{matrix}{{\Psi_{{(s)},k}^{soft}(x)} = \left\{ \begin{matrix}{c_{{(s)},k}x} \\{or} \\{c_{{(s)},k}\left( {{\tanh \left( {a_{{(s)},k}{Re}\left\{ x \right\}} \right)} + {\sqrt{- 1}{\tanh \left( {a_{{(s)},k}{Im}\left\{ x \right\}} \right)}}} \right)}\end{matrix} \right.} & {{Equation}\mspace{14mu} 32}\end{matrix}$

for positive real-valued constants a_((s), k) and C_((s), k). Theexpression Re{•} returns the real part of its argument, and Im{•}returns the imaginary part of its argument. A hard-decision map isprovided when Ψ_((s), k)(x) slices the input so that the output is anelement from the complex symbol constellation employed by the k^(th)user of base station (s),

$\begin{matrix}{{\Psi_{{(s)},k}^{hard}(x)} = {{{slice}(x)}.}} & {{Equation}\mspace{14mu} 33}\end{matrix}$

The slicer quantizes its argument x to the nearest constellation symbolaccording to some metric (e.g., Euclidean distance). A hard decision isapplicable only to those symbols whose constellations are known to thereceiver.

A mixed-decision map

Ψ_((s), k)^(mixed)(x)

produces an output that is a soft decision or a hard decision, such as

$\begin{matrix}{{\Psi_{{(s)},k}^{mixed}(x)} = \left\{ \begin{matrix}{\Psi_{{(s)},k}^{hard}(x)} & {{{{if}\mspace{14mu} {SINR}_{{(s)},k}}\rangle}c_{{(s)},k}} \\{\Psi_{{(s)},k}^{soft}(x)} & {otherwise}\end{matrix} \right.} & {{Equation}\mspace{14mu} 34}\end{matrix}$

The mixed-decision map

Ψ_((s), k)^(mixed)(x)

produces a hard decision if the SINR of a k^(th) user of base station(s) exceeds a threshold c_((s), k). Otherwise, a soft decision isperformed. The SINR maybe estimated with a time-averaged error-vectormeasurement (EVM). Time averaging may cause a block of symbols to sharethe same SINR estimate.

An alternative mixed-decision map

Ψ_((s), k)^(mixed)(x)

may act on individual symbols,

$\begin{matrix}{{\Psi_{{(s)},k}^{mixed}(x)} = \left\{ \begin{matrix}{\Psi_{{(s)},k}^{hard}(x)} & {{{if}\mspace{14mu} x} \in {C_{{(s)},k}\left( {{slice}(x)} \right)}} \\{\Psi_{{(s)},k}^{soft}(x)} & {{otherwise},}\end{matrix} \right.} & {{Equation}\mspace{14mu} 35}\end{matrix}$

where the constellation space for the symbol of a k^(th) user of basestation (s) is partitioned into hard- and soft-decision regions withC_((s),k)(b) denoting the hard-decision region for a symbol b from thatuser's constellation. If xεC_((s), k)(b), then a hard decision for x ismade. One embodiment for defining C_((s), k)(b) is to include all pointswithin a predetermined distance of b in the constellation space,

C _((s),k)(b)={x:distance(x,b)

c _((s),k)(b)},  Equation 36

where any distance metric may be used (e.g., |x−b|^(p) for some p>0) andthe radii c_((s), k)(b) over the set of constellation points b arechosen such that the hard-decision regions are non-overlapping.Alternative embodiments of the invention may employ different partitionsof the constellation space. For example, edge constellation points maybe given unbounded hard-decision regions.

Both the average SINR and instantaneous approaches are applicable to anyknown constellation; they need not be restricted to BPSK, QPSK, or evenQAM. Either of these mixed-decision approaches may be performed with theadditional constraint that the receiver knows only the constellationemployed for a subset of the active codes. Such situations may arise inEV-DO and HSDPA networks. In such cases, the receiver may use softdecisions for codes employing an unknown modulation. Those skilled inthe art will understand that a modulation classification of these codesmay be performed, which may be particularly useful in systems whereinall interfering codes share the same unknown constellation.

The following algorithm, which is illustrated in FIG. 13, demonstratesone embodiment for performing IIC.

Algorithm 1: Purpose: Estimate the

$K = {\sum\limits_{s = 0}^{B - 1}K_{(s)}}$

symbol in {circumflex over (b)}.Notation: The iteration index is represented by a superscript [i]; i=−1is the initialization; i=0 corresponds to the output of the front-endprocessor; and i>0 corresponds to the i-th ICU.Definitions: b in Equation 22

-   -   {circumflex over (b)} ^([i]) is in Equation 31    -   q is in Equation 17    -   R is in Equation 19    -   F is I or as in Equation 21    -   Γ^([i]) is in Equation 14 with elements defined in Equation        3-Equation 5    -   μ^([i]) is defined in Equation 23-Equation 27    -   Ψ maps each argument to a complex number to implement de-biasing        as in Equation 28-Equation 30 and then symbol estimation as in        Equation 32-Equation 36        Initializations: {circumflex over (b)} ^([−1])=0, a K×1 zero        vector    -   Γ^([−1])=I, a K×K identity matrix    -   μ^([−1])=1        Iterations: Index i=−1, 0, 1, . . . , M−1, where M is the number        of times to iterate the succeeding update equation        Update Equation: {circumflex over (b)}        ^([i+1])=Ψ{μ^([i])(q−RΓ^([i]) {circumflex over (b)}        ^([i]))+FΓ^([i]) {circumflex over (b)} ^([i])}        Output: {circumflex over (b)}={circumflex over (b)} ^([M]), the        symbol estimates after M iterations of the update equation.

FIG. 13 shows an internal feedback loop comprising operations 1308,1301, 1302, 1306, and an external feedback loop comprising operations1308, 1301, 1303, and 1304. The output of the external feedback loop isq−RΓ^([i]) {circumflex over (b)} ^([i]), which is multiplicativelyscaled 1305 by μ^([i]). The scaled output is combined 1306 with theinternal feedback loop to yield (q−RΓ^([i]) {circumflex over (b)}^([i]))+FΓ^([i]) {circumflex over (b)} ^([i]), which is processed by asymbol estimator 1307 and fed to the iteration delay 1308 that beginsthe internal and external loops.

Although embodiments of the invention are described with respect toforward-link channels, embodiments may be configured to operate inreverse-link channels. In the reverse link, different users'transmissions experience different multipath channels, which requiresappropriate modifications to Rake processing and signal synthesis. Forexample, a front-end processor may incorporate one Rake for every userin every base station rather than a single Rake per base station.Similarly, a separate multipath channel emulator may be employed forimparting multipath delays and gains to each user's signal. Accordingly,the number of constituent finger signals will equal the sum over thenumber of multipath fingers per user per base station, rather than thesum over the number of multipath fingers per base station.

It is clear that this algorithm may be realized in hardware or softwareand there are several modifications that can be made to the order ofoperations and structural flow of the processing.

Those skilled in the art should recognize that method and apparatusembodiments described herein may be implemented in a variety of ways,including implementations in hardware, software, firmware, or variouscombinations thereof. Examples of such hardware may include ApplicationSpecific Integrated Circuits (ASICs), Field Programmable Gate Arrays(FPGAs), general-purpose processors, Digital Signal Processors (DSPs),and/or other circuitry. Software and/or firmware implementations of theinvention may be implemented via any combination of programminglanguages, including Java, C, C++, MATLAB™, Verilog, VHDL, and/orprocessor specific machine and assembly languages.

Computer programs (i.e., software and/or firmware) implementing themethod of this invention may be distributed to users on a distributionmedium such as a SIM card, a USB memory interface, or othercomputer-readable memory adapted for interfacing with a consumerwireless terminal. Similarly, computer programs may be distributed tousers via wired or wireless network interfaces. From there, they willoften be copied to a hard disk or a similar intermediate storage medium.When the programs are to be run, they may be loaded either from theirdistribution medium or their intermediate storage medium into theexecution memory of a wireless terminal, configuring an onboard digitalcomputer system (e.g., a microprocessor) to act in accordance with themethod of this invention. All these operations are well known to thoseskilled in the art of computer systems.

The functions of the various elements shown in the drawings, includingfunctional blocks labeled as “modules” may be provided through the useof dedicated hardware, as well as hardware capable of executing softwarein association with appropriate software. When provided by a processor,the functions may be performed by a single dedicated processor, by ashared processor, or by a plurality of individual processors, some ofwhich may be shared. Moreover, explicit use of the term “processor or“module” should not be construed to refer exclusively to hardwarecapable of executing software, and may implicitly include, withoutlimitation, digital signal processor DSP hardware, read-only memory(ROM) for storing software, random access memory (RAM), and non-volatilestorage. Other hardware, conventional and/or custom, may also beincluded. Similarly, the function of any component or device describedherein may be carried out through the operation of program logic,through dedicated logic, through the interaction of program control anddedicated logic, or even manually, the particular technique beingselectable by the implementer as more specifically understood from thecontext.

The method and system embodiments described herein merely illustrateparticular embodiments of the invention. It should be appreciated thatthose skilled in the art will be able to devise various arrangements,which, although not explicitly described or shown herein, embody theprinciples of the invention and are included within its spirit andscope. Furthermore, all examples and conditional language recited hereinare intended to be only for pedagogical purposes to aid the reader inunderstanding the principles of the invention. This disclosure and itsassociated references are to be construed as applying without limitationto such specifically recited examples and conditions. Moreover, allstatements herein reciting principles, aspects, and embodiments of theinvention, as well as specific examples thereof, are intended toencompass both structural and functional equivalents thereof.Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture, i.e., any elements developed that perform the same function,regardless of structure.

1. An interference canceller configured for computinginterference-cancelled symbol estimates, and performing processingduring each of at least one iteration, the canceller comprising adecision module, the decision module configured for processing each ofone or more input symbol estimates for producing one of a hard decisionor a soft decision, and a de-biasing module configured for scaling theinput symbol estimates with a scale factor to remove bias computed onthe input symbol estimates for producing de-biased input symbolestimates.